This section describes approaches that could be employed, but are not necessarily approaches that have been previously conceived or employed. Hence, unless explicitly specified otherwise, any approaches described in this section are not prior art to the claims in this application, and any approaches described in this section are not admitted to be prior art by inclusion in this section.
Video-based analytics can be used for identifying various features and objects from images captured by cameras. Such video-based analytics can be used for different video recognition applications (i.e., recognition uses), for example monitoring manufacturing processes along a production line in a factory, video surveillance such as trespass monitoring, in-store shopper behavior monitoring, stadium security, crisis detection (e.g., fire, flood, theft, personal injury, earthquake, etc.), vehicle traffic monitoring, wildlife monitoring, etc. Different executable recognition resources have been developed to provide improved video-based analytics for the different video recognition applications; for example, Scale-Invariant Feature Transform (SIFT) is an executable recognition resource optimized for highly-accurate feature detection, but suffers from slow performance relative to the Speeded-Up Robust Features (SURF) executable recognition resource, which is much faster than SIFT but suffers from less accuracy than SIFT.
A camera device can be installed with one particular executable recognition resource, typically during manufacture of the camera device, or based on manual installation of the particular executable recognition resource during deployment of the camera device. The installed executable recognition resource, however, may be sub-optimal or inadequate for a particular video recognition application (i.e., recognition use) encountered by the camera device at a particular moment in time.